A New Approach That Selects a Single Hyperplane from the Optimal Pairwise Linear Classifier

  • Luis Rueda
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2905)

Abstract

In this paper, we introduce a new approach to selecting the best hyperplane classifier (BHC) from the optimal pairwise linear classifier is given. We first propose a procedure for selecting the BHC, and analyze the conditions in which the BHC is selected. In one of the cases, it is formally shown that the BHC and Fisher’s classifier (FC) are coincident. The empirical and graphical analysis on synthetic data and real-life datasets from the UCI machine learning repository, which involves the optimal quadratic classifier, the BHC, the optimal pairwise linear classifier, and FC.

Keywords

Random Vector Linear Discriminant Analysis Covariance Matrice Machine Intelligence Linear Dimensionality Reduction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Luis Rueda
    • 1
  1. 1.School of Computer ScienceUniversity of WindsorWindsorCanada

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